Evaluation of Dynamic Response and Brain Deformation Metrics for a Helmeted and Non-Helmeted Hybrid III Headform Using a Monorail Centric/Non-Centric Protocol
Bibliographic record
Abstract
Head injuries, and concussion in particular, have become a source of interest in the sport of ice hockey. This study proposes a monorail test methodology combined with a finite element method to evaluate ice hockey helmets in a centric/non-centric protocol with performance metrics more closely associated with risk of concussion. Two conditions were tested using the protocol: (a) helmeted versus no helmet, and (b) vinyl nitrile lined hockey helmet versus expanded polypropylene lined hockey helmet. The results indicate that the impact velocities and locations produced distinct responses. Also, the protocol distinguished important design characteristics of the two helmet liner types, with the vinyl nitrile lined helmet producing lower strain responses in the cerebrum. Furthermore, it was discovered that low risk of injury peak linear and rotational acceleration values can combine to produce much higher risks of injury when using brain deformation metrics. In conclusion, the use of finite element modeling of the human brain along with a centric/non-centric protocol provides an opportunity for researchers and helmet developers to observe how the dynamic response produced by these impacts influences brain tissue deformation and injury risk. This type of centric/non-centric physical to finite element modeling methodology could be used to guide innovation for new methods to prevent concussion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".